A better strategy is to consider both the importance and novelty of a sentence. A classic implementation of the strategy is the MMR (maximal marginal relevance) method [1]}. At each iteration, MMR selects one sentence from \(\mathcal {D}\)  and includes it in the summary \(Y\)  until a length limit is reached. The selected sentence \(S_i\)  is the most important one among the remaining sentences and it has the least content overlap with the current summary. As in Equation REF  below, \(\mathrm {sim1}(S_i , \mathcal {D})\)  measures the similarity of the sentence \(S_i\)  to the documents, indicating the sentence importance. \(\max _{S_j \in Y} \mathrm {sim2}(S_i , S_j)\)  measures the maximum similarity of the sentence \(S_i\)  to each of the summary sentences, acting as a proxy of redundancy. \(\lambda \)  is a balancing factor.
\(\mathrm {MMR} \triangleq \arg \max _{S_{i} \in \mathcal {D} \backslash Y}\left[\lambda \mathrm {sim1}(S_i, \mathcal {D}) - (1-\lambda )\max _{S_j \in Y}\mathrm {sim2}(S_i, S_j)\right]\) 
